Typically, speech/audio codecs process low- and high-frequency components of an audio signal with different compression schemes. Most of the available bit-budget is consumed by the LB (Low frequency Band) coder (due to the higher sensitivity of human auditory system at these frequencies). In addition to that, most of the available computational complexity is also consumed by the LB codec, e.g., analysis-by-synthesis ACELP (Algebraic Code Excited Linear Prediction). This leaves severe requirements on the complexity available to the HB (High frequency Band) codec.
Due to the mentioned above constraints the HB part of the signal is typically reconstructed by a parametric BWE (Band Width Extension) algorithm. This solution handles the problem of the constrained bit-budget and limited complexity, but it completely lacks scalability, which means that the quality quickly saturates and does not follow the bit-rate increase.
Variable bit-rate schemes such as entropy coding schemes present an efficient way to encode sources at a low average bit-rate. However, many applications rely on a fixed bit-rate for the encoded signal, such as e.g. mobile communication channels. The number of consumed bits for a segment of a given input signal is not known before the entropy coding has been completed. One common solution is to run several iterations of the entropy coder until a good compression ratio within the fixed bit budget has been reached.
Consequently, there is a need for methods and arrangements to enable low-complexity and scalable coding of the high band part of audio signals and enables utilizing a variable bit-rate quantization scheme within a fixed bitrate framework.
The solution of running multiple iterations for the entropy coder is a computationally complex solution, which may not fit in the context of real-time communication on a device with limited processing power.